The key challenge in egocentric gaze estimation is to effectively integrate multiple gaze cues into a holistic analysis of visual attention. Cues include the likelihood that different scene objects are gaze targets (i.e. salience), the relative location of gaze targets within the video frame (i.e. center prior), and the patterns of camera movement that are reflective of visual attention (i.e. head motions accompanying a gaze shift). Recently, the transformer has achieved great success in various vision tasks by modeling the spatio-temporal correlation among local visual tokens [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. However, the pairwise comparisons performed by standard transformer Self-Attention (SA) are not optimized for interpreting local video features in the context of the global scene. The example Fig. REF  illustrates the key role of comparisons between local patches and global context - the gaze target is a salient object pointed at by both the camera wearer and another person. Such a salient object can not be easily localized by only modeling the correlation of local patches.
